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In this paper, we introduce a novel approach to enhance the accuracy and convergence behavior of Self-Organizing Maps (SOM) by incorporating a reweighted zero-attracting term into the loss function. We evaluated two SOM versions: conventional SOM and robust adaptive SOM (RASOM). The enhanced versions, reweighted zero-attracting SOM (RZA-SOM) and reweighted zero-attracting RASOM (RZA-RASOM), include an l 1 norm in the error function to add a zero-attractor term, which improves weight coefficient adjustments while preserving topology. The models were assessed for convergence speed and misadjustment under sparsity assumptions of the true coefficient matrix, and their robustness was tested under conditions of increased non-zero taps. Using six different datasets, we compared the performance of RZA-SOM and RZA-RASOM against conventional SOM and RA-SOM in terms of accuracy, quantization error, and topology preservation. Experimental results consistently demonstrated that RZA-SOM and RZA-RASOM surpassed the performance of conventional SOM and RA-SOM. • Introduction of novel reweighted zero-attractor term in loss function for heightened SOM algorithmic precision. • Heightened convergence rates and improved behavior, marked by earlier error steady-state attainment during training. • Enhanced robustness against outliers and superior handling of imbalanced data in multiclass clustering scenarios. • Rigorous experimental evaluations across six distinct datasets effectively underpin proposed algorithmic enhancements. [ABSTRACT FROM AUTHOR]

Water quality degradation poses a significant challenge globally, especially in developing nations like Sri Lanka. Extensive monitoring programs designed to address escalating river pollution collect multiple water quality parameters over extended periods and varied locations. However, the sheer volume of data can be overwhelming, making it difficult to process effectively and interpret accurately using conventional methods. In this study, latent variable (LV) and unsupervised machine learning techniques were used to investigate spatial and seasonal variations of surface water quality for 17 parameters across 17 locations along the Kelani River, Sri Lanka, using monthly water quality parameters from 2016 to 2020. Pearson's correlation matrix identified 10 parameters significantly affecting water quality variations and factor analysis (FA) generated five LVs, accounting for 77% of the total variance in the dataset. The identified LVs showed multiple methods of river pollution. Hierarchical clustering analysis and self-organizing mapping methods clustered stations in a closely analogous manner. Stations near industrial zones and the river mouth showed higher water quality variance, often exceeding national guidelines. Correlation testing revealed strong relationships between water quality and catchment hydrometeorological variations during monsoonal seasons. Spatial analyses showed increased LV variance in the Lower Kelani River Basin, indicating higher pollutant levels in different seasons. Industrial effluents (LV-2 and LV-4) and domestic and municipal sewage (LV-3 and LV-5) exhibit greater seasonal fluctuations. The results showed that the proposed LV approach has the potential to assist authorities in addressing water pollution amidst the complexity of multiple water quality parameters. [ABSTRACT FROM AUTHOR]

Variable heat fluxes over the Tibetan Plateau (TP) interact thermally with the atmosphere, affecting weather in surrounding areas, particularly in the Middle and Lower Yangtze River (MLYR). However, the circulation patterns and time-lag effects between TP heat fluxes and MLYR precipitation remain unclear. This study identified 577 large-scale daily heavy precipitation events (LSDHPEs) in the MLYR from 1980 to 2022. We analyzed the weather causation and spatiotemporal correlations between the TP surface heat fluxes and MLYR LSDHPEs using self-organizing map clustering, singular value decomposition, and harmonic analysis of time series. The results found two dominant synoptic patterns of LSDHPEs at 500 hPa: one, driven by anticyclonic and cyclonic circulations coinciding with shifts in the West Pacific subtropical high and South Asian high, increased from 2000 to 2022; the other, influenced by MLYR cyclonic circulation, showed a significant decrease. For the first time, we revealed lagged effects of the latent heat anomalies (with a lag time of 1–10 d and 130–200 d) and sensible heat anomalies (with a lag time of 2–4 months) over the TP during LSDHPEs in the MLYR. The results may enhance our understanding of TP heat flux anomalies as precursor signals for early warning of heavy rainfall and flooding in the MLYR. [ABSTRACT FROM AUTHOR]

This study introduces a deep self-organizing map neural network based on level-set (LS-SOM) for the customization of a shoe-last defined from plantar pressure imaging data. To alleviate the over-segmentation problem of images, which refers to segmenting images into more subcomponents, a domain-based segmentation model of plantar pressure images was constructed. The domain growth algorithm was subsequently modified by optimizing its parameters. A SOM with 10, 15, 20, and 30 hidden layers was compared and validated according to domain growth characteristics by using merging and splitting algorithms. Furthermore, we incorporated a level set segmentation method into the plantar pressure image algorithm to enhance its efficiency. Compared to the literature, this proposed method has significantly improved pixel accuracy, average cross-combination ratio, frequency-weighted cross-combination ratio, and boundary F1 index comparison. Using the proposed methods, shoe lasts can be designed optimally, and wearing comfort is enhanced, particularly for people with high blood pressure. [ABSTRACT FROM AUTHOR]

Cognitive diagnosis is a crucial element of intelligent education that aims to assess the proficiency of specific skills or traits in students at a refined level and provide insights into their strengths and weaknesses for personalized learning. Researchers have developed numerous cognitive diagnostic models. However, previous studies indicate that diagnostic accuracy can be significantly influenced by the appropriateness of the model and the sample size. Thus, designing a general model that can adapt to different assumptions and sample sizes remains a considerable challenge. Artificial neural networks have been proposed as a promising approach in some studies. In this paper, we propose a cognitive diagnosis model of a neural network constrained by a Q-matrix and named QNN. Specifically, we employ the Q-matrix to determine the connections between neurons and the width and depth of the neural network. Moreover, to reduce the human effort in the training algorithm, we designed a self-organizing map-based cognitive diagnosis training framework called SOM-NN, which enables the QNN to be trained unsupervised. Extensive experimental results on simulated and real datasets demonstrate that our approaches are effective in both accuracy and interpretability. Notably, under unsupervised conditions, our approach has significant advantages on small sample datasets with high levels of guessing and slipping, especially on the pattern-wise agreement rates. This work bridges the gap between psychometrics and machine learning and provides a realistic and implementable reference solution for classroom instructional assessment and the cold start of personalized and adaptive assessment systems. [ABSTRACT FROM AUTHOR]

This study evaluates different input variable selection (IVS) methods for precise daily precipitation estimation using artificial neural network (ANN) models. The effectiveness of the models is measured using the average Nash-Sutcliffe efficiency (NSE) from 50 runs per model. Initially, a multiple linear regression and a base model with all 16 parameters resulted in test NSE scores of 0.390 and 0.656, respectively. Subsequently, the self-organizing map (SOM), analytic hierarchy process, K-nearest neighbour, and K-means methods were used to select input variables of different types. The SOM method achieved its best performance with a test NSE of 0.692 when four variables were selected, while the other methods reached their peak performance with the same NSE of 0.718 when two similar variables were selected. This highlights the importance of IVS methods in using ANN models to overcome overfitting and achieve an efficient model for precise precipitation estimation. [ABSTRACT FROM AUTHOR]

When an artificial structure is built in a river, the river changes significantly in water quality and hydraulic properties. In this study, the effects of the weirs constructed in the middle section of a river as a four major rivers restoration project in Korea on water quality and hydrological characteristics were analyzed. For multi‐dimensional data analysis, a self‐organizing map was applied, and statistical techniques including analysis of variation were used. As a result of analysis, the cross‐sectional area of the river increased significantly after the construction of the weir compared to before the construction of the weir, and the flow velocity decreased at a statistically significant level. In the case of water quality, nitrogen, phosphorus, and suspended solids tended to improve after weir construction, and chlorophyll‐a and bacteria tended to deteriorate. Some water quality parameters such as chlorophyll‐a were also affected by seasonal influences. In order to improve the water quality deteriorated by the construction of the weir, it is necessary to consider how to improve the flow velocity of the river through partial opening or operation of the weir. In addition, in order to determine the effect of sedimentation of particulate matter due to the decrease in flow rate, it is necessary to conduct investigations on sediments around weirs in the future. Practitioner Points: Compared to before the construction of the weir, there was no significant change in the flow rate of the river after the construction of the weir.In the case of chlorophyll‐a and bacteria, the water quality was deteriorated after weir construction.To improve the deteriorated water quality, it is required to consider the fundamental management of each pollutant source and the flexible operation of both weirs.For some improved water quality parameters, further research is needed to determine whether these improvements are directly attributable to the construction of a weir. [ABSTRACT FROM AUTHOR]

Ecosystems offer natural resources and habitats for humans, serving as the foundation for human social development. Taking the Tianshan Mountains as the study area, this study investigated the changing trends, hot spots, and driving factors of water yield (WY), soil conservation (SC), carbon storage (CS), and habitat quality (HQ), in the Tianshan region, from 1990 to 2020. To determine the trade-offs and synergies between the ESs, we employed the Spearman correlation coefficient, geographically weighted regression, the self-organizing map (SOM), and other methods. Five main results were obtained. (1) There were similar spatial distribution patterns for WY, HQ, CS, and SC, with high-value areas mainly concentrated in grassland zones, forest zones, river valleys, and the intermountain basins of the mountain range, while regions with low value were clustered in desert zones and snow/ice zones. (2) According to the hotspot analysis, areas with relatively strong ES provisioning for WY, HQ, CS, and SC, were primarily concentrated in the BoroHoro Ula Mountains and Yilianhabierga Mountains. In contrast, areas with relatively weak ES provisioning were mainly located in the Turpan Basin. (3) Precipitation was the primary explanatory factor for WY. Soil type, potential evapotranspiration (PET), and the normalized difference vegetation index (NDVI) were the primary explanatory factors for HQ. Soil type and NDVI were the primary explanatory factors for CS. PET was the primary explanatory factor for SC. (4) There were synergistic relationships between the WY, HQ, CS, and SC, with the strongest synergies found between CS–HQ, WY–HQ, and WY–SC. (5) Six ES bundles were identified through the SOM method, with their composition varying at different spatial scales, indicating the need for different ES management priorities in different regions. Our analysis of ESs, from various perspectives, offers insights to aid sustainable ecosystem management and conservation efforts in the Tianshan region and other major economic areas worldwide. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel approach to enhance the accuracy and convergence behavior of Self-Organizing Maps (SOM) by incorporating a reweighted zero-attracting term into the loss function. We evaluated two SOM versions: conventional SOM and robust adaptive SOM (RASOM). The enhanced versions, reweighted zero-attracting SOM (RZA-SOM) and reweighted zero-attracting RASOM (RZA-RASOM), include an l1 norm in the error function to add a zero-attractor term, which improves weight coefficient adjustments while preserving topology. The models were assessed for convergence speed and misadjustment under sparsity assumptions of the true coefficient matrix, and their robustness was tested under conditions of increased non-zero taps. Using six different datasets, we compared the performance of RZA-SOM and RZA-RASOM against conventional SOM and RA-SOM in terms of accuracy, quantization error, and topology preservation. Experimental results consistently demonstrated that RZA-SOM and RZA-RASOM surpassed the performance of conventional SOM and RA-SOM.

Background With the population aging, the number of patients with chronic diseases and disability is increasing, which brings a heavy burden on the medical system. While it is well established that comorbidity and disability are closely interconnected, there is a noticeable scarcity of studies addressing the relationship between different patterns of comorbidity and disability. Objective To explore the common comorbidity patterns and their associations with disability among the older adults in China taking Sichuan as an example. Methods A total of 501 older adults, aged 60 and above, were selected using quota sampling in Sichuan from August to Novermber 2022. Detailed data on chronic diseases, disability, and general demographics were collected. Comorbidity patterns among the older adults were identified using a two-step clustering method that combined self-organizing maps and K-Means. The association between patterns of comorbidity and disability was explored using a logistic regression model, employing the National Disability Assessment Scale for the Long-term Care issued by the National Healthcare Security Administration of China in 2021. Results In this survey, The prevalence of comorbidity 62.3% (312/501) and the prevalence of disability was 74.3% (372/501). We identified six comorbidity patterns: Arthritis/Rheumatism and Hypertension diseases, Cardiovascular and Metabolic diseases, Kidney and Arthritis/Rheumatism diseases, Cancer and Arthritis/Rheumatism diseases, Asthma, Hypertension, and Gastrointestinal diseases, Emotional/Mental and Memory-related diseases. The results of binomial logistic regression showed that the risk of disability was 6.3 times higher when people suffering from two or more chronic diseases at the same time (OR=6.3, 95%CI=3.9-10.3, P

Mini-batch partitioning is a widely used technique in deep learning that involves dividing a dataset into smaller subsets. This method is crucial in training deep learning models such as deep neural networks and convolutional neural networks. It is favored for its ability to accelerate model convergence, reduce memory overhead, and minimize convergence errors. The primary advantage of mini-batch partitioning is that it allows the model to learn dataset features more evenly, thereby speeding up the convergence process. However, determining the optimal method and size for mini-batch partitioning remains a challenging problem. This paper proposes a novel mini-batch partitioning method focused on feature reorganization. By leveraging a Self-Organizing Map network for feature extraction, data with similar characteristics are initially grouped into the same batch. The purity index of each batch is then calculated based on the number of features and labels, allowing for a comprehensive evaluation of batch homogeneity. Batches with significant differences in purity are selectively reorganized to ensure that each batch contains a diverse set of features, reducing intra-batch feature correlation and ultimately enhancing data representation.Furthermore, through SOM network mapping, the dataset can be effectively partitioned into subsets that are well-suited for model training. Experimental comparisons of various batch partitioning methods on multiple UCI datasets demonstrate that our proposed method, termed MPBS (Mini-Batch Partitioning Algorithm based on Self-Organizing Map Network). Compared with other algorithms, the accuracy, loss and training time are improved by 14.06%, 24.31% and 31.22%. [ABSTRACT FROM AUTHOR]

This study investigated the characteristics of dissolved organic matter (DOM) in two distinct water bodies, through the utilization of three-dimensional fluorescence spectroscopy coupled with self-organizing map (SOM) methodology. Specifically, this analysis concentrated on neurons 3, 14, and 17 within the SOM model, identifying notable differences in the DOM compositions of a coal subsidence water body (TX) and the MaChang Reservoir (MC). The humic substance content of DOM TX exceeded that of MC. The origin of DOM in TX was primarily linked to agricultural inputs and rainfall runoff, whereas the DOM in MC was associated with human activities, displaying distinctive autochthonous features and heightened biological activity. Principal component analysis revealed that humic substances dominated the DOM in TX, while the natural DOM in MC was primarily autochthonous. Furthermore, a multiple linear regression model (MLR) determined that external pollution was responsible for 99.11% of variation in the humification index (HIX) of water bodies. HIGHLIGHTS Study of the variation in DOM spectral characteristics of different waterbodies.; Neural networks were applied to spectral resolution.; Source analysis was performed to identify the origin of water pollution.;

Previous studies examining the flora in the Korean Demilitarized Zone and its surroundings focused only on specific locations or ways to utilize these areas. We investigated vascular plant distribution in this area across the entire Korean Peninsula. Species richness, endemism, and alien species richness were examined for 54,948 occurrence records. Biodiversity and protection status were assessed, and a self-organizing map algorithm was used to divide the flora distribution into an Eastern region (including the Central region) and a Western region. Environmental factors of each distribution type were analyzed. The whole study area had 1,790 taxa of 655 genera and 151 families. The flora of the Central region was similar to that of the Eastern region, which had the richest biodiversity, whereas the Western region contained various invasive alien plants. Most areas with high biodiversity were classified as protected by international agreements. In the self-organizing map, the Eastern and Central regions exhibited large distribution of endemic and northern lineage plants, and the Western region showed high abundances of invasive alien plants. The considerable biodiversity of this area is threatened due to continuous housing development and military operations. Therefore, policies to ensure sustainable conservation of plant biodiversity “hotspots” are required.

With the widespread use of unmanned aerial vehicles (UAVs), the detection and identification of UAVs is a vital security issue for the safety of airspace and ground facilities in the no-fly zone. Telemetry radios are important wireless communication devices for UAVs, especially in UAVs beyond the visual line of sight (BVLOS) operating mode. This work focuses on the UAV identification approach using transient signals from UAV telemetry radios instead of the signals from UAV controllers that the former research work depended on. In our novel UAV Radio Frequency (RF) identification system framework based on telemetry radio signals, the E C − α algorithm is optimized to detect the starting point of the UAV transient signal and the detection accuracy at different signal-to-noise ratios (SNR) is evaluated. In the training stage, the Convolutional Neural Network (CNN) model is trained to extract features from raw I/Q data of the transient signals with different waveforms. Its architecture and hyperparameters are analyzed and optimized. In the identification stage, the extracted transient signals are clustered through the Self-Organizing Map (SOM) algorithm and the Clustering Signals Joint Identification (CSJI) algorithm is proposed to improve the accuracy of RF fingerprint identification. To evaluate the performance of our proposed approach, we design a testbed, including two UAVs as the flight platform, a Universal Software Radio Peripheral (USRP) as the receiver, and 20 telemetry radios with the same model as targets for identification. Indoor test results show that the optimized identification approach achieves an average accuracy of 92.3% at 30 dB. In comparison, the identification accuracy of SVM and KNN is 69.7% and 74.5%, respectively, at the same SNR condition. Extensive experiments are conducted outdoors to demonstrate the feasibility of this approach. [ABSTRACT FROM AUTHOR]

It is challenging to interpret hydrogeochemical datasets with complex natural and anthropogenic genesis in intensive industrial areas. This paper elucidates the hydrogeochemical characteristics and pollution sources of groundwater in an industrial park, East China, combining the self-organizing map (SOM), hydrochemical graphs, and correlation analysis. The results show that the total dissolved solids of groundwater range from 73.45 to 997.92 mg/L and can be regarded as freshwater. The pH varies greatly from 6.44 to 9.90, most of samples belonging to weakly acidic-weakly alkaline. The groundwater can be classified into five clusters by SOM, representing the non- or least-polluted groundwater (cluster D), high salt groundwater (cluster A), high NH4+-N and HCO3− groundwater (cluster B), high Fe and Mn groundwater (cluster C), and high pH groundwater (cluster E), which were contaminated by industrial salts, historical agriculture activity, industrial reducing substances, and industrial alkali, respectively. The natural evolution of groundwater (cluster D) in the study area is mainly controlled by mineral weathering/dissolution. The contributions of calcite, dolomite, gypsum, halite, and silicate mineral to groundwater solute are 55.8–66.3%, 15.1–18.0%, 9.0–10.7%, 2.5–10.1%, and 2.3–9.4%, respectively, based on the mass conservation. The contaminated groundwaters (all other clusters except for cluster D) have different hydrochemical characteristics associated with the pollution sources. In addition, the relatively reductive environment in quaternary flu-lacustrine sediments favored the formation of high level of Fe, Mn, and NH4+-N in groundwater. This study provides a new insight into the characteristic contaminants and their distributions in groundwater and the associated pollution sources based on the large datasets in an intensive industrial area. The data evaluation methods and results of this study could be useful to the groundwater usage management and pollution control in this area and other industrial areas. [ABSTRACT FROM AUTHOR]

This study verifies the capability of evolutionary circulation to characterize the movement of rain belt based on Self-Organizing Map (SOM) and further explores the interdecadal variation of rain belt's northward advancement during flood season in eastern China. The results show that the evolving path of circulation patterns can effectively describe the trajectory of rain belt's movement with a significant correlation coefficient of 0.91. By employing the time required for circulation evolution to represent the velocity of rain belt's movement, the northward velocity of rain belt emerges interdecadal shifts around the mid-late1970s and the late 1990s, which is affected by the sea surface temperature anomaly of Pacific Decadal Oscillation (PDO) pattern. During negative PDO phases, the warming of western North Pacific and the cooling in equatorial middle-east Pacific trigger the northward westerly jet and Western Pacific Subtropical High, leading to the northward vertical upward motion. This configuration encourages the circulation pattern corresponding to Yangtze-Huaihe River basin rain belt to transfer to that representing more precipitation in North and Northeast China, resulting in the accelerated northward advance of rain belt. Conversely, during positive PDO phases, the moisture brought by westward subtropical high and upward motion driven by the southward upper jet stream increase the likelihood of persistent precipitation in Yangtze-Huaihe River Basin, slowing the advancement of rain belt. During the phase transition of PDO, the northward leap of circulation systems in July has been proved to play a pivotal role in the interdecadal variation of rain belt's overall advancing velocity. [ABSTRACT FROM AUTHOR]

Multimodal retrieval has gained much attention lately due to its effectiveness over uni-modal retrieval. For instance, visual features often under-constrain the description of an image in content-based retrieval; however, another modality, such as collateral text, can be introduced to abridge the semantic gap and make the retrieval process more efficient. This article proposes the application of cross-modal fusion and retrieval on real in vivo gastrointestinal images and linguistic cues, as the visual features alone are insufficient for image description and to assist gastroenterologists. So, a cross-modal information retrieval approach has been proposed to retrieve related images given text and vice versa while handling the heterogeneity gap issue among the modalities. The technique comprises two stages: (1) individual modality feature learning; and (2) fusion of two trained networks. In the first stage, two self-organizing maps (SOMs) are trained separately using images and texts, which are clustered in the respective SOMs based on their similarity. In the second (fusion) stage, the trained SOMs are integrated using an associative network to enable cross-modal retrieval. The underlying learning techniques of the associative network include Hebbian learning and Oja learning (Improved Hebbian learning). The introduced framework can annotate images with keywords and illustrate keywords with images, and it can also be extended to incorporate more diverse modalities. Extensive experimentation has been performed on real gastrointestinal images obtained from a known gastroenterologist that have collateral keywords with each image. The obtained results proved the efficacy of the algorithm and its significance in aiding gastroenterologists in quick and pertinent decision making. [ABSTRACT FROM AUTHOR]

Peripheral neuropathy (PN) is a severe and frequent complication of obesity, prediabetes, and type 2 diabetes characterized by progressive distal-to-proximal peripheral nerve degeneration. However, a comprehensive understanding of the mechanisms underlying PN, and whether these mechanisms change during PN progression, is currently lacking. Here, gene expression data were obtained from distal (sciatic nerve; SCN) and proximal (dorsal root ganglia; DRG) injury sites of a high-fat diet (HFD)-induced mouse model of obesity/prediabetes at early and late disease stages. Self-organizing map and differentially expressed gene analyses followed by pathway enrichment analysis identified genes and pathways altered across disease stage and injury site. Pathways related to immune response, inflammation, and glucose and lipid metabolism were consistently dysregulated with HFD-induced PN, irrespective of injury site. However, regulation of oxidative stress was unique to the SCN while dysregulated Hippo and Notch signaling were only observed in the DRG. The role of the immune system and inflammation in disease progression was supported by an increase in the percentage of immune cells in the SCN with PN progression. Finally, when comparing these data to transcriptomic signatures from human patients with PN, we observed conserved pathways related to metabolic dysregulation across species, highlighting the translational relevance of our mouse data. Our findings demonstrate that PN is associated with distinct site-specific molecular re-programming in the peripheral nervous system, identifying novel, clinically relevant therapeutic targets. [ABSTRACT FROM AUTHOR]

In many Unmanned Aerial Vehicle (UAV) operations, accurately estimating the UAV's position and orientation over time is crucial for controlling its trajectory. This is especially important when considering the landing maneuver, where a ground-based camera system can estimate the UAV's 3D position and orientation. A Red, Green, and Blue (RGB) ground-based monocular approach can be used for this purpose, allowing for more complex algorithms and higher processing power. The proposed method uses a hybrid Artificial Neural Network (ANN) model, incorporating a Kohonen Neural Network (KNN) or Self-Organizing Map (SOM) to identify feature points representing a cluster obtained from a binary image containing the UAV. A Deep Neural Network (DNN) architecture is then used to estimate the actual UAV pose based on a single frame, including translation and orientation. Utilizing the UAV Computer-Aided Design (CAD) model, the network structure can be easily trained using a synthetic dataset, and then fine-tuning can be done to perform transfer learning to deal with real data. The experimental results demonstrate that the system achieves high accuracy, characterized by low errors in UAV pose estimation. This implementation paves the way for automating operational tasks like autonomous landing, which is especially hazardous and prone to failure. [ABSTRACT FROM AUTHOR]

Buildings emit a great deal of carbon dioxide and use a lot of energy. The study of building energy consumption is useful for the sustainable development of multi-energy planning and energy-saving strategies. Therefore, a sustainable development for building energy consumption based on the improved rafflesia optimization algorithm (ROA) with feature selection and ensemble deep learning is proposed in this paper. This method can explore data on building energy usage, assess prediction accuracy, and address concerns that building energy usage research must address. The proposed model first uses an improved self-organizing map with a new neighborhood function to select important features. After that, it uses ensemble deep learning to accurately anticipate the building's energy usage. In addition, the improved ROA is used to fine-tune parameters for feature selection and ensemble deep learning. This research uses the dataset of the American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) to compare the performance of several modeling approaches. It identifies the top five most important features based on the model's results. Furthermore, the proposed model can be successfully applied to a real-world application. They both have the lowest root mean squared errors among the approaches examined. The proposed model indeed provides the benefits of feature selection and ensemble deep learning with the improved ROA for the prediction of building energy consumption. [ABSTRACT FROM AUTHOR]

Fluoride-enriched groundwater is a serious threat for groundwater supply around the world. The medium–low temperature fluoride-enriched geothermal groundwater resource is widely distributed in the circum-Wugongshan area. And the fluoride concentration of all geothermal samples exceeds the WHO permissible limit of 1.5 mg/L. The Self-Organizing Map method, hydrochemical and isotopic analysis are used to decipher the driving factors and genetic mechanism of fluoride-enriched geothermal groundwater. A total of 19 samples collected from the circum-Wugongshan geothermal belt are divided into four clusters by the self-organizing map. Cluster I, Cluster II, Cluster III, and Cluster IV represent the geothermal groundwater with the different degree of fluoride concentration pollution, the different hydrochemical type, and the physicochemical characteristic. The high F− concentration geothermal groundwater is characterized by HCO3-Na with alkalinity environment. The δD and δ18O values indicate that the geothermal groundwater origins from the atmospheric precipitation with the recharge elevation of 1000-2100 m. The dissolution of fluoride-bearing minerals is the main source of fluoride ions in geothermal water. Moreover, groundwater fluoride enrichment is also facilitated by water–rock interaction, cation exchange and alkaline environment. Additionally, the health risk assessment result reveals that the fluorine-enriched geothermal groundwater in the western part of Wugongshan area poses a more serious threat to human health than that of eastern part. The fluoride health risks of geothermal groundwater for different group show differentiation, 100% for children, 94.74% for adult females, and 68.42% for adult males, respectively. Compared with adult females and adult males, children faced the greatest health risks. The results of this study provide scientific evaluation for the utilization of geothermal groundwater and the protection of human health around the Wugongshan area. [ABSTRACT FROM AUTHOR]

The white blood cell (WBC) plays an instrumental role in protecting the human body from different infectious diseases and unknown biological invaders. Their count and cellular morphology drastically change when the body suffers from any such disease or abnormal conditions. This paper demonstrates the detection of five types of WBC present in human blood smear images. Several authors proposed WBC detection using only one color space. But this paper addresses WBC-type detection using four popular color spaces – RGB (red, green, and blue), HSV (hue, saturation, and intensity), YCbCr (luminance, chroma blue, and chroma red), and L*a*b* (luminosity, chromaticity layer "a*", and chromaticity layer "b*"). The proposed methodology involves color thresholding, K-means clustering, nuclear region analysis, and the implementation of a feedforward neural network. The unsupervised machine learning algorithm, K-means clustering is used to segment nuclei from the blood smear images. The detection of WBC-type using four color spaces is compared in terms of accuracy, sensitivity, specificity, and positive predictive value. It is observed that HSV-based WBC-type detection methodology outperforms methodologies based on RGB, YCbCr, and L*a*b* color spaces. The same methodology mentioned above is then compared with the works proposed by other authors. [ABSTRACT FROM AUTHOR]

The amount of winter precipitation in Japan is increasing only in the Kanto-Koshin region, which includes the Tokyo metropolitan area located on the Pacific Ocean side of Japan. This study investigated long-term historical changes in winter precipitation in Japan using a large ensemble of regional climate simulations that capture the observed increases in precipitation in the Kanto-Koshin region. In addition, self-organizing maps (SOMs) were applied to the atmospheric pressure to explore historical changes in weather pattern (WP) leading to precipitation. The results suggest that long-term changes in precipitation/snowfall could be due to differences in the WP frequency of southern-coastal cyclones and WP-related precipitation due to altered background states during the winter. The former and latter can be interpreted as dynamic and thermodynamic impacts of historical climate change. As for the Kanto-Koshin region, atmospheric circulation analogs revealed that thermodynamic and dynamic effects account for ~ 2/3 and ~ 1/3 of the precipitation changes, respectively, while dynamic effects (increases in south coast cyclone occurrence) dominantly account for snowfall changes. These changes could be attributed to the large-scale historical increases in air temperature and water vapor content. [ABSTRACT FROM AUTHOR]

This study aimed to develop a method for assessing landscapes using environmental data and user-generated data, which are commonly employed in landscape research. It focused on the Seoul metropolitan area in South Korea, devising evaluation indicators for five key concepts: naturalness, diversity, imageability, historicity, and disturbance. These indicators were used to assess the landscapes based on each index. We employed a self-organizing map, an artificial neural network technique, to categorize the landscape units and developed eight evaluation indicators for the five key concepts, organizing the study area's landscapes into six distinct landscape units. This study identified landscape unit types with increased vulnerability to visual blight or heightened public awareness by considering both user characteristics and environmental attributes in the metropolitan area landscapes. Finally, we discussed future tasks for appropriate landscape management based on each landscape area's characteristics to maintain and enhance landscape quality. [ABSTRACT FROM AUTHOR]

Content-based image retrieval (CBIR) is a significant research area focused on developing effective techniques for managing large image collections and facilitating efficient retrieval. The primary steps in CBIR involve extracting features from both query and database images and identifying database images with similar extracted features to the query image. The choice of feature extraction methods is crucial, and similarity matching is equally important. The quality of retrieval results depends on the design of the similarity matching process, as it determines how well the system matches the query image's features with those in the database, ranks highly matched database images, and presents them in the retrieval process. In this paper, an innovative content-based image retrieval framework is proposed, which is known as 'SOM based multi-stage CBIR method using the features of CPVTH, POPMV and MDLBP integrated with Multi-Query (CBIR_SCPMMQ).' By incorporating the core concepts of CPVTH, POPMV, MDLBP, and SOM, along with a multi-stage and multi-query-based retrieval system, this proposed CBIR framework offers a dynamic approach to the world of computer vision. Experimental tests on various image databases, including natural, medical, remote sensing, and texture images, have shown that CBIR_SCPMMQ outperforms the existing CBIR methods in terms of precision and retrieval accuracy. The proposed CBIR_SCPMMQ method demonstrates the improvements in precision by 9.03%, recall by 8.3%, sensitivity by 8.6%, accuracy by 2.01%, and FScore by 8.02%. Therefore, the proposed CBIR_SCPMMQ method outperforms other established CBIR techniques. [ABSTRACT FROM AUTHOR]

Most studies in the field of land use and land cover (LULC) classification in remote sensing rely on supervised classification, which requires a substantial amount of accurate label data. However, reliable data are often not immediately available, and are obtained through time-consuming manual labor. One potential solution to this problem is the use of already available classification maps, which may not be the true ground truth and may contain noise from multiple possible sources. This is also true for the classification maps of the MapBiomas project, which provides land use and land cover (LULC) maps on a yearly basis, classifying the Amazon basin into more than 24 classes based on the Landsat data. In this study, we utilize the Sentinel-2 data with a higher spatial resolution in conjunction with the MapBiomas maps to evaluate a proposed noise removal method and to improve classification results. We introduce a novel noise detection method that relies on identifying anchor points in feature space through clustering with self-organizing maps (SOM). The pixel label is relabeled using nearest neighbor rules, or can be removed if it is unknown. A challenge in this approach is the quantification of noise in such a real-world dataset. To overcome this problem, highly reliable validation sets were manually created for quantitative performance assessment. The results demonstrate a significant increase in overall accuracy compared to MapBiomas labels, from 79.85% to 89.65%. Additionally, we trained the L2HNet using both MapBiomas labels and the filtered labels from our approach. The overall accuracy for this model reached 93.75% with the filtered labels, compared to the baseline of 74.31%. This highlights the significance of noise detection and filtering in remote sensing, and emphasizes the need for further research in this area. [ABSTRACT FROM AUTHOR]

The co-variability in the summer precipitation and cloud-to-ground lightning over South China, where climatological lighting and precipitation frequently occur (105～122 ° E, 20～35 ° N), has been examined using the ADTD lightning location network and precipitation data merged ground-gauge, satellite, and radar. The coupling self-organizing map (SOM) has been applied to the daily flash density and precipitation anomalies from 2016 to 2021. It turns out that there are five clusters associate with the co-variability in the flash density and precipitation over South China. Specifically, the first (fifth) cluster is characterized by a dipole pattern with enhanced rainfall / increased lightning activity (suppressed rainfall / decreased lightning activity) over the Yangtze-Huai River valley and suppressed rainfall/decreased lightning activity (enhanced rainfall/increased lightning activity) over the southern part of South China. The tightest lightning-precipitation coincidence is observed in the first and fifth clusters. Results obtained from SOM exhibit large non-linearity and asymmetry of the relationship between the regional lightning and precipitation anomalies over South China. Further examinations suggest that unstable atmospheric stratifications, significant vertical differences in the atmospheric humidity, and beneficial large-scale atmospheric circulations play important roles in modulating the co-variability in the summer lightning and precipitation over South China. [ABSTRACT FROM AUTHOR]

Forming optimal peer groups is a crucial step in multiplier valuation. Among others, the traditional regression methodology requires the definition of the optimal set of peer selection criteria and the optimal size of the peer group a priori. Since there exists no universally applicable set of closed and complementary rules on selection criteria due to the complexity and the diverse nature of firms, this research exclusively examines unlisted companies, rendering direct comparisons with existing studies impractical. To address this, we developed a bespoke benchmark model through rigorous regression analysis. Our aim was to juxtapose its outcomes with our unique approach, enriching the understanding of unlisted company transaction dynamics. To stretch the performance of the linear regression method to the maximum, various datasets on selection criteria (full as well as F- and NCA-optimized) were employed. Using a sample of over 20,000 private firm transactions, model performance was evaluated employing multiplier prediction error measures (emphasizing bias and accuracy) as well as prediction superiority directly. Emphasizing five enterprise and equity value multiples, the results allow for the overall conclusion that the self-organizing map algorithm outperforms the traditional linear regression model in both minimizing the valuation error as measured by the multiplier prediction error measures as well as in direct prediction superiority. Consequently, the machine learning methodology offers a promising way to improve peer selection in private firm multiplier valuation. [ABSTRACT FROM AUTHOR]

Abstract The pollution of surface water and groundwater is a real public health problem that is currently receiving particular attention throughout the world. The use of water for food or hygiene purposes requires excellent physicochemical and microbiological quality. Well water is used for many purposes by the inhabitants of M’pody, a village in the Anyama district of Côte d’Ivoire. In this village, an epidemic of diarrhoea was detected in January 2020. This epidemic claimed sixty-nine (69) victims. This study aims to evaluate well water quality controls using the methods of principal component analysis (PCA), correspondence factorial analysis (CFA), analysis of variance (ANOVA) and self-organizing map (SOM) algorithm. The parameters studied were, turbidity (Tur), conductivity (Cond), hydrogen potential (pH), temperature (T), nitrate ( $$NO_{3^{-}}$$ N O 3 - ), nitrite ( $$NO_{2^{-}}$$ N O 2 - ), ammonium ( $$NH_{4^{+}}$$ N H 4 + ), phosphates ( $$PO_{4}^{3^{-}}$$ P O 4 3 - ), chlorides $$(Cl^{-})$$ ( C l - ) , total hydrotimetric degree (DHT), sulfates ( $$SO_{4}^{2^{-}}$$ S O 4 2 - ), bicarbonate ( $$HCO_{3^{-}}$$ H C O 3 - ), total alkalinity contents (TAC), escherichia coli (E.coli), thermotolerant coliforms (CTH) and enterococcus faecalis (E.faecalis). Data were collected from seventy-two (72) wells in the village during four campaigns in 2020. Physicochemical parameters were determined by electrochemical and spectrophotometric methods. Microbiological analysis was carried out using membrane filtration technique. Descriptive statistics revealed that Tur, Cond, pH and T parameters did not meet world health organization (WHO, 2017) standards. However, the parameters $$NO_{3^{-}}$$ N O 3 - , $$NO_{2^{-}}$$ N O 2 - , $$NH_{4^{+}}$$ N H 4 + , $$PO_{4}^{3^{-}}$$ P O 4 3 - , $$Cl^{-}$$ C l - , DHT, TAC, $$SO_{4}^{2^{-}}$$ S O 4 2 - and $$HCO_{3^{-}}$$ H C O 3 - comply with WHO standards. The results of bacteriological analyzes confirm the presence in very significant numbers of indicators of faecal contamination (CTH, E.coli and E.faecalis) in all wells. The logical explanations for faecal pollution would come from the infiltration of septic tanks located near the wells and the run-off of waste water carrying human and animal faecal matter. The diarrhea epidemic would therefore be caused by the consumption of this polluted water. PCA, FCA and hierarchical cluster analysis (HCA) were jointly employed to identify the structure of wells and deduce the principal factors controlling the parameters of these well waters. ANOVA revealed the effect of human-induced activities as the main factors influencing the physicochemical and microbiological parameters of the studied well waters. Further studies should focus on multivariate statistical techniques for effective forecasting and monitoring of emerging pollution for improved water quality.

Digital image segmentation plays an important role in noise reduction and pixel clustering for pre-processing of deep learning or feature extraction. The classic Self-Organizing Map (SOM) algorithm is a well-known unsupervised clustering neural network model. This classic method works on continuous data instead of discrete data sets with a widely scattered distribution. The novel SOM(SOM2) modelling solved this problem for the classic, simple tabular discrete data set but not for the digital image data. As the essence of digital image pixels data are different from tabular datasets, we have to look at them differently. This paper proposes exploiting the novel SOM method with a hybrid combination of the fuzzy C-Means and K-means convolution filter as image segmentation and noise reduction with soft and hard segmentation as entropy reduction for natural digital images. The main approach of this paper is the segmentation of image contents for the reduction of noises and saturation pixels by entropy criteria. Based on the resulting paper, the combination of SOM2 with FCM for soft segmentation 47%-and the combination of SOM2 with k-means convolution for hard segmentation 33% can reduce the entropy of the original image on average.

Abstract Introduction Data clustering is an important field of machine learning that has applicability in wide areas, like, business analysis, manufacturing, energy, healthcare, traveling, and logistics. A variety of clustering applications have already been developed. Data clustering approaches based on self‐organizing map (SOM) generally use the map dimensions (of the grid) ranging from 2 × 2 to 8 × 8 (4–64 neurons [microclusters]) without any explicit reason for using the particular dimension, and therefore optimized results are not obtained. These algorithms use some secondary approaches to map these microclusters into the lower dimension (actual number of clusters), like, 2, 3, or 4, as the case may be, based on the optimum number of clusters in the specific data set. The secondary approach, observed in most of the works, is not SOM and is an algorithm, like, cut tree or the other. Methods In this work, the proposed approach will give an idea of how to select the most optimal higher dimension of SOM for the given data set, and this dimension is again clustered into the lower actual dimension. Primary and secondary, both utilize the SOM to cluster the data and discover that the weight matrix of the SOM is very meaningful. The optimized two‐dimensional configuration of SOM is not the same for every data set, and this work also tries to discover this configuration. Results The adjusted randomized index obtained on the Iris, Wine, Wisconsin diagnostic breast cancer, New Thyroid, Seeds, A1, Imbalance, Dermatology, Ecoli, and Ionosphere is, respectively, 0.7173, 0.9134, 0.7543, 0.8041, 0.7781, 0.8907, 0.8755, 0.7543, 0.5013, and 0.1728, which outperforms all other results available on the web and when no reduction of attributes is done in this work. Conclusions It is found that SOM is superior to or on par with other clustering approaches, like, k‐means or the other, and could be used successfully to cluster all types of data sets. Ten benchmark data sets from diverse domains like medical, biological, and chemical are tested in this work, including the synthetic data sets.

Abstract Parkinson's Disease (PD) is a complex, degenerative disease that affects nerve cells that are responsible for body movement. Artificial Intelligence (AI) algorithms are widely used to diagnose and track the progression of this disease, which causes symptoms of Parkinson's disease in its early stages, by predicting the results of the Unified Parkinson's Disease Rating Scale (UPDRS). In this study, we aim to develop a method based on the integration of two methods, one complementary to the other, Ensembles of Self-Organizing Map and Neuro-Fuzzy, and an unsupervised learning algorithm. The proposed method relied on the higher effect of the variables resulting from the analysis of the initial readings to obtain a correct and accurate preliminary prediction. We evaluate the developed approach on a PD dataset including speech cues. The process was evaluated with root mean square error (RMSE) and modified R square (modified R2). Our findings reveal that the proposed method is effective in predicting UPDRS outcomes by a combination of speech signals (measures of hoarseness). As the preliminary results during the evaluation showed numbers that proved the worth of the proposed method, such as UPDRS = 0.955 and RMSE approximately 0.2769 during the prediction process.

Non-target fish are frequently present in crayfish aquaculture systems, yet limited research has explored their predatory impacts on crayfish larvae. To address this gap, three experiments were conducted to examine the predation dynamics of Rhodeus sinensis on Procambarus clarkii larvae, focusing on the effects of predator sizes, group sizes, and aquatic plant refuges. In experiment 1, Cox proportional hazards regression model revealed that P. clarkii larvae (8.40 mm and 11.30 mm) faced heightened predation risks from R. sinensis, particularly when the predator’s weight exceeded 2 g. Predation mortality significantly rose with increasing predator size. P. clarkii larvae primarily sought refuge in bottom corners and under heating rods, as indicated by Self-Organizing Map. Experiment 2, analyzed with Bayesian Additive Regression Tree, demonstrated that group predation of R. sinensis significantly enhanced predatory efficiency on larger crayfish larvae, resulting in reduced time to the first attack, increased total number of attacks and extended total attack duration. Experiment 3 disclosed that casualty and mortality rates of crayfish larvae markedly decreased when aquatic plants coverage exceeded 20 %, irrespective of plants types. Under predation pressure, crayfish larvae exhibited a preference for microhabitats on and under aquatic plants, indicating an adaptive behavior in response to predation. These findings underscore the susceptibility of P. clarkii larvae to predation by R. sinensis and suggest maintaining a 20 % aquatic plant coverage as an effective management strategy to mitigate predation risks and support juvenile crayfish survival in aquaculture systems.

Study Region: Tongde Basin, a typical alpine basin on the northeastern Tibetan Plateau Study focus: Research on the hydrochemical pattern and availability of groundwater is very limited in alpine regions, which greatly hinders the rational ulitilization and scientific management of groundwater resources. Multiple approaches integrating self-organizing map, K-means, entropy weight were employed to elucidate the distinct hydrochemical evolution patterns and controlling mechanisms of groundwater in alpine basins with dense human activities. New hydrological insights for the region: Multiple hydrochemical evolution patterns were discovered for groundwater in present alpine basin on northeastern Tibetan Plateau. Groundwater presents a gradual hydrochemical evolution from the piedmont to the riverside under the natural regulation with the increasing of TDS and major ions along the flow path. While, groundwater at some sporadic sites is featured with relatively high total alkalinity due to carbonate mineral dissolution. The riverine groundwaters are regulated by the strong hydrodynamic condition of river and demonstrate fresher hydrochemical characteristics. Groundwaters in the irrigation areas are featured by elevated nitrate levels with a maximum of 42.86 mg/L, attributed to agricultural pollution inputs. A conceptual model was established to illustrate the distinct hydrochemical fingerprints, evolution patterns and controlling mechanisms of groundwater in alpine basins that with dense human activities. This research can enhance the understanding of groundwater chemical status, genesis and availability, and is beneficial for the conservation and sustainable management of groundwater resources in ecologically fragile alpine regions worldwide.

Study region: A typical estuarine delta of Gan−Xiu River within Poyang Lake watershed, situated in the north of Jiangxi Province, China Study focus: Although groundwater is considered as a crucial water resource for social development and public health, it presents significant challenges in preventing the deterioration of groundwater quality in the seasonal floodplain regions. Hence, this study focuses on the spatiotemporal variations of ionic concentrations of shallow groundwater in an estuarine delta of Gan-Xiu River in Poyang Lake watershed, which is greatly complicated by complex hydrological regimes and intensive anthropogenic activities such as cropland fertilizer. The potential factors controlling the ionic concentrations were identified using self-organizing map clustering and Piper diagram approaches. Moreover, the overall water quality of shallow groundwater was assessed according to the ionic compositions. New hydrological insights for the region: (1) Shallow groundwater chemistry was influenced by weathering silicate and carbonate from ionic and Gibbs plots, which is highly associated with the distribution of the carbonate rock fissure-karst aquifer in the study region. (2) The contribution of groundwater to river water varied from 53.2 % to 71.5 % via isotopic analysis, suggesting that groundwater dominate the component of river water. (3) Most samples from cluster types are suitable for drinking and irrigation use except for sites from Cluster 6, which were characterized by the higher concentrations of Cl−, NO3− and SO42−.

Introduction: Modeling plays a crucial role in understanding wastewater treatment processes, yet conventional deterministic models face challenges due to complexity and uncertainty. Artificial intelligence offers an alternative, requiring no prior system knowledge. This study tested the reliability of the Adaptive Fuzzy Inference System (ANFIS), an artificial intelligence algorithm that integrates both neural networks and fuzzy logic principles, to predict effluent Biochemical Oxygen Demand. An important indicator of organic pollution in wastewater. Materials and Methods: The ANFIS models were developed and validated with historical wastewater quality data for the Kauma Sewage Treatment Plant located in Lilongwe City, Malawi. A Self Organizing Map (SOM) was applied to extract features of the raw data to enhance the performance of ANFIS. Cost-effective, quicker, and easier-to-measure variables were selected as possible predictors while using their respective correlations with effluent. Influents' temperature, pH, dissolved oxygen, and effluent chemical oxygen demand were among the model predictors. Results and Discussions: The comparative results demonstrated that for the same model structure, the ANFIS model achieved correlation coefficients (R) of 0.92, 0.90, and 0.81 during training, testing, and validation respectively, whereas the SOM-assisted ANFIS Model achieved R Values of 0.99, 0.87 and 0.94. Overall, despite the slight decrease in R-value during the testing stage, the SOM- assisted ANFIS model outperformed the traditional ANFIS model in terms of predictive capability. A graphic user interface was developed to improve user interaction and friendliness of the developed model. Integration of the developed model with supervisory control and data acquisition system is recommended. The study also recommends widening the application of the developed model, by retraining it with data from other wastewater treatment facilities and rivers in Malawi. [ABSTRACT FROM AUTHOR]

The pollution of surface water and groundwater is a real public health problem that is currently receiving particular attention throughout the world. The use of water for food or hygiene purposes requires excellent physicochemical and microbiological quality. Well water is used for many purposes by the inhabitants of M'pody, a village in the Anyama district of Côte d'Ivoire. In this village, an epidemic of diarrhoea was detected in January 2020. This epidemic claimed sixty-nine (69) victims. This study aims to evaluate well water quality controls using the methods of principal component analysis (PCA), correspondence factorial analysis (CFA), analysis of variance (ANOVA) and self-organizing map (SOM) algorithm. The parameters studied were, turbidity (Tur), conductivity (Cond), hydrogen potential (pH), temperature (T), nitrate ( N O 3 - ), nitrite ( N O 2 - ), ammonium ( N H 4 + ), phosphates ( P O 4 3 - ), chlorides (C l -) , total hydrotimetric degree (DHT), sulfates ( S O 4 2 - ), bicarbonate ( H C O 3 - ), total alkalinity contents (TAC), escherichia coli (E.coli), thermotolerant coliforms (CTH) and enterococcus faecalis (E.faecalis). Data were collected from seventy-two (72) wells in the village during four campaigns in 2020. Physicochemical parameters were determined by electrochemical and spectrophotometric methods. Microbiological analysis was carried out using membrane filtration technique. Descriptive statistics revealed that Tur, Cond, pH and T parameters did not meet world health organization (WHO, 2017) standards. However, the parameters N O 3 - , N O 2 - , N H 4 + , P O 4 3 - , C l - , DHT, TAC, S O 4 2 - and H C O 3 - comply with WHO standards. The results of bacteriological analyzes confirm the presence in very significant numbers of indicators of faecal contamination (CTH, E.coli and E.faecalis) in all wells. The logical explanations for faecal pollution would come from the infiltration of septic tanks located near the wells and the run-off of waste water carrying human and animal faecal matter. The diarrhea epidemic would therefore be caused by the consumption of this polluted water. PCA, FCA and hierarchical cluster analysis (HCA) were jointly employed to identify the structure of wells and deduce the principal factors controlling the parameters of these well waters. ANOVA revealed the effect of human-induced activities as the main factors influencing the physicochemical and microbiological parameters of the studied well waters. Further studies should focus on multivariate statistical techniques for effective forecasting and monitoring of emerging pollution for improved water quality. [ABSTRACT FROM AUTHOR]

Brain–computer interfaces (BCIs) allow information to be transmitted directly from the human brain to a computer, enhancing the ability of human brain activity to interact with the environment. In particular, BCI-based control systems are highly desirable because they can control equipment used by people with disabilities, such as wheelchairs and prosthetic legs. BCIs make use of electroencephalograms (EEGs) to decode the human brain's status. This paper presents an EEG-based facial gesture recognition method based on a self-organizing map (SOM). The proposed facial gesture recognition uses α , β , and θ power bands of the EEG signals as the features of the gesture. The SOM-Hebb classifier is utilized to classify the feature vectors. We utilized the proposed method to develop an online facial gesture recognition system. The facial gestures were defined by combining facial movements that are easy to detect in EEG signals. The recognition accuracy of the system was examined through experiments. The recognition accuracy of the system ranged from 76.90% to 97.57% depending on the number of gestures recognized. The lowest accuracy (76.90%) occurred when recognizing seven gestures, though this is still quite accurate when compared to other EEG-based recognition systems. The implemented online recognition system was developed using MATLAB, and the system took 5.7 s to complete the recognition flow. [ABSTRACT FROM AUTHOR]

Observation of the external 3D shape/structure and some measurable phenotypic traits is of great significance to screening excellent varieties and improving crop yield in agriculture. The dense crop point clouds scanned by 3D sensors not only may include imaging noise, but also contain a large number of redundant points that will put high burden on storage and slow down the speed of algorithm for point cloud segmentation, classification, and other following processing steps. To reduce the complexity of point cloud data and meanwhile better represent the structure under limited resources, this paper presents a new Self-organizing Map (SOM)-based down-sampling strategy that is tailored for plant (or plant-like) point clouds. Our SOM-based sampling works in a purely unsupervised manner and precisely controls the number of points after down-sampling. It obtains shape-aware sampling on irregular plant point clouds by automatically encoding preliminary semantics to different organ types (e.g., stems are sampled as "lines", and leaves are sampled as folded curved shaped in "surfaces"). Extensive experiments on a multi-species plant dataset were conducted using several popular deep 3D-segmentation networks as the downstream task unit, respectively. The segmentation performance of the SOM-processed dataset outperformed several other mainstream down-sampling strategies. Our SOM strategy with 1D neuron layer can be further generalized to 2D and 3D versions, and also can be extended to a more adaptive framework that automatically picks the most suitable version of SOM for each corresponding local shape component. The proposed strategy also showed good potential in serving different applications including point cloud skeleton extraction, crop main stem length measurement; and presented satisfactory results on point cloud datasets from other domains, indicating its high applicability and good data domain adaptation. [ABSTRACT FROM AUTHOR]

The use of heart sounds for the assessment of the hemodynamic condition of the heart in telemonitoring applications is object of wide research at date. Many different approaches have been tried out for the analysis of the first (S1) and second (S2) heart sounds, but their morphological interpretation is still to be explored: in fact, the sound morphology is not unique and this impact the separability of the heart sounds components with methods based on envelopes or model optimization. In this study, we propose a method to stratify S1 and S2 according to their morphology to explore their diversity and increase their morphological interpretability. The method we propose is based on unsupervised learning, which we obtain using the cascade of four Self-Organizing Maps (SOMs) of decreasing dimensions. When tested on a publicly available heart sounds dataset, the proposed clustering approach proved to be robust and consistent, with over 80% of the heartbeats of the same patient being clustered together. The identified heart sounds templates highlight differences in the time and energy domains which may open to new directions of analysis in the future. [ABSTRACT FROM AUTHOR]

In automatic speech recognition (ASR) systems, the minimization of noxious effects caused by different background noises between training and operating situations has been a challenging task for many years. An ASR robust to noise that can deal with different types of speeches and various speakers still is an open research point. Typically, conventional ASR models for missing-feature reconstructions and robust speech descriptors employ acoustic features and statistical methods. In spite of improved performance in dealing with noise, such methods still degrade the performance when different background noises co-exist with the main signal. More recent approaches use neural networks, particularly deep learning models, for ASR purposes. Such models increase performance at the high training cost. In order to mitigate such limitations, we proposed an ASR model called Self-Organizing Speech Recognizer (SOSR). Unlike most conventional ASRs, SOSR is characterized by using acoustic and articulatory features, employing unsupervised and incremental learning, and is suitable for real-time applications due to its quick training stage. SOSR simultaneously processes an audio signal in a two-branch. In the first path, the acoustic features are extracted from the original signal whereas in the second path an acoustic-to-articulatory inversion is performed by several Self-organizing Maps. The signal from both paths is delivered to a Self-organizing Map with a time-varying structure, which is responsible for recognizing the input speech signal. Four datasets (TIMIT, Aurora 2, Aurora 4, and CHIME 2) were used for SOSR assessment. The Word Error Rate (WER) was the chosen metric to compare the experimental results of the tests with different noise levels and signal variations. Hence, the experimental results suggest that SOSR can learn quickly, and it can handle noisy signals, various speakers, different types of speeches, and assorted lengths of utterances. [ABSTRACT FROM AUTHOR]

Continuum robots play the role of end effectors in various surgical robots and endoscopic devices. While soft continuum robots (SCRs) have proven advantages such as safety and compliance, more research and development are required to enhance their capability for specific medical scenarios. This research aims at designing a soft robot, considering the concepts of geometric and kinematic similarities. The chosen application is a semi-invasive medical application known as transesophageal echocardiography (TEE). The feasibility of fabrication of a soft endoscopic device derived from the Chorda dorsalis paragon was shown empirically by producing a three-segment pneumatic SCR. The main novelties include bioinspired design, modeling, and a navigation control strategy presented as a novel algorithm to maintain a kinematic similarity between the soft robot and the rigid counterpart. The kinematic model was derived based on the method of transformation matrices, and an algorithm based on a self-organizing map (SOM) network was developed and applied to realize kinematic similarity. The simulation results indicate that the control method forces the soft robot tip to follow the path of the rigid probe within the prescribed distance error (5 mm). The solution provides a soft robot that can surrogate and succeed the traditional rigid counterpart owing to size, workspace, and kinematics. [ABSTRACT FROM AUTHOR]

Parkinson's Disease (PD) is a complex, degenerative disease that affects nerve cells that are responsible for body movement. Artificial Intelligence (AI) algorithms are widely used to diagnose and track the progression of this disease, which causes symptoms of Parkinson's disease in its early stages, by predicting the results of the Unified Parkinson's Disease Rating Scale (UPDRS). In this study, we aim to develop a method based on the integration of two methods, one complementary to the other, Ensembles of Self-Organizing Map and Neuro-Fuzzy, and an unsupervised learning algorithm. The proposed method relied on the higher effect of the variables resulting from the analysis of the initial readings to obtain a correct and accurate preliminary prediction. We evaluate the developed approach on a PD dataset including speech cues. The process was evaluated with root mean square error (RMSE) and modified R square (modified R2). Our findings reveal that the proposed method is effective in predicting UPDRS outcomes by a combination of speech signals (measures of hoarseness). As the preliminary results during the evaluation showed numbers that proved the worth of the proposed method, such as UPDRS = 0.955 and RMSE approximately 0.2769 during the prediction process. [ABSTRACT FROM AUTHOR]

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Digital image segmentation plays an important role in noise reduction and pixel clustering for pre-processing of deep learning or feature extraction. The classic Self-Organizing Map (SOM) algorithm is a well-known unsupervised clustering neural network model. This classic method works on continuous data instead of discrete data sets with a widely scattered distribution. The novel SOM(SOM2) modelling solved this problem for the classic, simple tabular discrete data set but not for the digital image data. As the essence of digital image pixels data are different from tabular datasets, we have to look at them differently. This paper proposes exploiting the novel SOM method with a hybrid combination of the fuzzy C-Means and K-means convolution filter as image segmentation and noise reduction with soft and hard segmentation as entropy reduction for natural digital images. The main approach of this paper is the segmentation of image contents for the reduction of noises and saturation pixels by entropy criteria. Based on the resulting paper, the combination of SOM2 with FCM for soft segmentation 47%-and the combination of SOM2 with k-means convolution for hard segmentation 33% can reduce the entropy of the original image on average. [ABSTRACT FROM AUTHOR]